During inference, we can increase the capacity of greedy search via limited width breadth-first beam search, which maintains the \(b\)  most probable tours during decoding.
Similarly, we can sample \(b\)  solutions from the learnt policy and select the shortest tour among them.
Naturally, searching longer, with more sophisticated techniques [1]}, [2]}, or sampling more solutions allows trading off run time for solution quality.
However, it has been noted that using large \(b\)  for search/sampling or local search during inference may overshadow an architecture's inability to generalize [3]}.
To better understand generalization, we focus on using greedy search and beam search/sampling with small \(b = 128\) .
